DATA
6de0bf9f5b
Vendored deer-flow upstream (bytedance/deer-flow) plus prompt-injection hardening: - New deerflow.security package: content_delimiter, html_cleaner, sanitizer (8 layers — invisible chars, control chars, symbols, NFC, PUA, tag chars, horizontal whitespace collapse with newline/tab preservation, length cap) - New deerflow.community.searx package: web_search, web_fetch, image_search backed by a private SearX instance, every external string sanitized and wrapped in <<<EXTERNAL_UNTRUSTED_CONTENT>>> delimiters - All native community web providers (ddg_search, tavily, exa, firecrawl, jina_ai, infoquest, image_search) replaced with hard-fail stubs that raise NativeWebToolDisabledError at import time, so a misconfigured tool.use path fails loud rather than silently falling back to unsanitized output - Native client back-doors (jina_client.py, infoquest_client.py) stubbed too - Native-tool tests quarantined under tests/_disabled_native/ (collect_ignore_glob via local conftest.py) - Sanitizer Layer 7 fix: only collapse horizontal whitespace, preserve newlines and tabs so list/table structure survives - Hardened runtime config.yaml references only the searx-backed tools - Factory overlay (backend/) kept in sync with deer-flow tree as a reference / source See HARDENING.md for the full audit trail and verification steps.
187 lines
6.8 KiB
Python
187 lines
6.8 KiB
Python
"""Tests for deerflow.models.patched_deepseek.PatchedChatDeepSeek.
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Covers:
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- LangChain serialization protocol: is_lc_serializable, lc_secrets, to_json
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- reasoning_content restoration in _get_request_payload (single and multi-turn)
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- Positional fallback when message counts differ
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- No-op when no reasoning_content present
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"""
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from __future__ import annotations
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from unittest.mock import MagicMock, patch
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from langchain_core.messages import AIMessage, HumanMessage
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def _make_model(**kwargs):
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from deerflow.models.patched_deepseek import PatchedChatDeepSeek
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return PatchedChatDeepSeek(
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model="deepseek-reasoner",
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api_key="test-key",
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**kwargs,
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)
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# ---------------------------------------------------------------------------
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# Serialization protocol
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# ---------------------------------------------------------------------------
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def test_is_lc_serializable_returns_true():
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from deerflow.models.patched_deepseek import PatchedChatDeepSeek
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assert PatchedChatDeepSeek.is_lc_serializable() is True
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def test_lc_secrets_contains_api_key_mapping():
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model = _make_model()
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secrets = model.lc_secrets
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assert "api_key" in secrets
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assert secrets["api_key"] == "DEEPSEEK_API_KEY"
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assert "openai_api_key" in secrets
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def test_to_json_produces_constructor_type():
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model = _make_model()
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result = model.to_json()
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assert result["type"] == "constructor"
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assert "kwargs" in result
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def test_to_json_kwargs_contains_model():
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model = _make_model()
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result = model.to_json()
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assert result["kwargs"]["model_name"] == "deepseek-reasoner"
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assert result["kwargs"]["api_base"] == "https://api.deepseek.com/v1"
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def test_to_json_kwargs_contains_custom_api_base():
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model = _make_model(api_base="https://ark.cn-beijing.volces.com/api/v3")
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result = model.to_json()
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assert result["kwargs"]["api_base"] == "https://ark.cn-beijing.volces.com/api/v3"
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def test_to_json_api_key_is_masked():
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"""api_key must not appear as plain text in the serialized output."""
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model = _make_model()
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result = model.to_json()
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api_key_value = result["kwargs"].get("api_key") or result["kwargs"].get("openai_api_key")
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assert api_key_value is None or isinstance(api_key_value, dict), f"API key must not be plain text, got: {api_key_value!r}"
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# ---------------------------------------------------------------------------
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# reasoning_content preservation in _get_request_payload
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# ---------------------------------------------------------------------------
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def _make_payload_message(role: str, content: str | None = None, tool_calls: list | None = None) -> dict:
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msg: dict = {"role": role, "content": content}
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if tool_calls is not None:
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msg["tool_calls"] = tool_calls
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return msg
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def test_reasoning_content_injected_into_assistant_message():
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"""reasoning_content from additional_kwargs is restored in the payload."""
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model = _make_model()
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human = HumanMessage(content="What is 2+2?")
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ai = AIMessage(
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content="4",
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additional_kwargs={"reasoning_content": "Let me think: 2+2=4"},
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)
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base_payload = {
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"messages": [
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_make_payload_message("user", "What is 2+2?"),
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_make_payload_message("assistant", "4"),
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]
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}
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with patch.object(type(model).__bases__[0], "_get_request_payload", return_value=base_payload):
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with patch.object(model, "_convert_input") as mock_convert:
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mock_convert.return_value = MagicMock(to_messages=lambda: [human, ai])
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payload = model._get_request_payload([human, ai])
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assistant_msg = next(m for m in payload["messages"] if m["role"] == "assistant")
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assert assistant_msg["reasoning_content"] == "Let me think: 2+2=4"
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def test_no_reasoning_content_is_noop():
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"""Messages without reasoning_content are left unchanged."""
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model = _make_model()
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human = HumanMessage(content="hello")
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ai = AIMessage(content="hi", additional_kwargs={})
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base_payload = {
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"messages": [
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_make_payload_message("user", "hello"),
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_make_payload_message("assistant", "hi"),
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]
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}
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with patch.object(type(model).__bases__[0], "_get_request_payload", return_value=base_payload):
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with patch.object(model, "_convert_input") as mock_convert:
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mock_convert.return_value = MagicMock(to_messages=lambda: [human, ai])
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payload = model._get_request_payload([human, ai])
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assistant_msg = next(m for m in payload["messages"] if m["role"] == "assistant")
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assert "reasoning_content" not in assistant_msg
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def test_reasoning_content_multi_turn():
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"""All assistant turns each get their own reasoning_content."""
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model = _make_model()
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human1 = HumanMessage(content="Step 1?")
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ai1 = AIMessage(content="A1", additional_kwargs={"reasoning_content": "Thought1"})
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human2 = HumanMessage(content="Step 2?")
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ai2 = AIMessage(content="A2", additional_kwargs={"reasoning_content": "Thought2"})
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base_payload = {
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"messages": [
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_make_payload_message("user", "Step 1?"),
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_make_payload_message("assistant", "A1"),
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_make_payload_message("user", "Step 2?"),
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_make_payload_message("assistant", "A2"),
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]
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}
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with patch.object(type(model).__bases__[0], "_get_request_payload", return_value=base_payload):
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with patch.object(model, "_convert_input") as mock_convert:
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mock_convert.return_value = MagicMock(to_messages=lambda: [human1, ai1, human2, ai2])
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payload = model._get_request_payload([human1, ai1, human2, ai2])
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assistant_msgs = [m for m in payload["messages"] if m["role"] == "assistant"]
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assert assistant_msgs[0]["reasoning_content"] == "Thought1"
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assert assistant_msgs[1]["reasoning_content"] == "Thought2"
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def test_positional_fallback_when_count_differs():
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"""Falls back to positional matching when payload/original message counts differ."""
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model = _make_model()
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human = HumanMessage(content="hi")
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ai = AIMessage(content="hello", additional_kwargs={"reasoning_content": "My reasoning"})
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# Simulate count mismatch: payload has 3 messages, original has 2
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extra_system = _make_payload_message("system", "You are helpful.")
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base_payload = {
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"messages": [
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extra_system,
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_make_payload_message("user", "hi"),
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_make_payload_message("assistant", "hello"),
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]
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}
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with patch.object(type(model).__bases__[0], "_get_request_payload", return_value=base_payload):
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with patch.object(model, "_convert_input") as mock_convert:
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mock_convert.return_value = MagicMock(to_messages=lambda: [human, ai])
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payload = model._get_request_payload([human, ai])
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assistant_msg = next(m for m in payload["messages"] if m["role"] == "assistant")
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assert assistant_msg["reasoning_content"] == "My reasoning"
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